Detecting early signs of failures (anomalies) in complex systems is one
of the main goal of preventive maintenance. It allows in particular to
avoid actual failures by (re)scheduling maintenance operations in a way
that optimizes maintenance costs. Aircraft engine health monitoring is
one representative example of a eld in which anomaly detection is crucial.
Manufacturers collect large amount of engine related data during
ights
which are used, among other applications, to detect anomalies. This arti-
cle introduces and studies a generic methodology that allows one to build
automatic early signs of anomaly detection in a way that builds upon hu-
man expertise and that remains understandable by human operators who
make the nal maintenance decision. The main idea of the method is
to generate a very large number of binary indicators based on parametric
anomaly scores designed by experts, complemented by simple aggregations
of those scores. A feature selection method is used to keep only the most
discriminant indicators which are used as inputs of a Naive Bayes classi-
er. This give an interpretable classier based on interpretable anomaly
detectors whose parameters have been optimized indirectly by the selec-
tion process. The proposed methodology is evaluated on simulated data
designed to reproduce some of the anomaly types observed in real world
engines.